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How to Create A Basic Markov Chain in R Programming
Forecasting a process using probabilities is part of the value Markov Chains bring to real-world experiences. Here’s how to set up a basic model in R.
Forecasting data can be a complex guessing game, especially when applying a chain of probabilities. One chain that makes a probability model useful is a Markov Chain. In simple terms, a Markov Chain is a probability sequence showing how likely conditions of a given set of probabilities will occur over a chain of instances.
I wrote about the concept of Markov Chains in a previous post— this post will explain the syntax using R programming and explain what a data model should look like.
What is a Markov Chain?
A Markov Chain is a stochastic probability model describing a “chain” of possible events. The chain is represented by a sequence of probabilities related to those events. The purpose of a Markov Chain is to determine the likelihood of those events holding up over time. A Markov Chain model acts as a steady-state predictor of the likelihood that certain events will happen based on the current state. The model helps us understand how things change as they move from one state to another.
